Kültürlerarası İletişimde Nöral Makine Çevirisinin İncelenmesi: Airbnb'nin Çeviri İşlevi Üzerine Bir Vaka Analizi
Öz
Bu çalışma, bir sanal platform olan Airbnb’nin kullanıcıların birbirlerinin anadilini bilmesine gerek kalmadan kültürlerarası iletişimi mümkün kılan otomatik çeviri işlevinin kuramsal ve pratik etkilerini incelemektedir. Kullanıcılar, bu uygulama ve internet sayfası üzerinden gerçekleştirilen iletişimde kullanılan dilin farkında olmadan, aynı dili konuşmadan sorunsuz bir şekilde iletişim kurabilmektedir. Yöntem kısmında, çalışmada sitenin dil ve çeviri politikalarının içerik analizi ile kullanıcı deneyimini ve çevirilerin dil bilgisel doğruluğunu değerlendirmek için vaka çalışmaları bir arada kullanılmaktadır. Çalışmanın bulguları, mevcut çeviri teknolojilerinin hem güçlü yönlerini hem de geliştirilmesi gereken alanlarını ortaya koyarak küresel platformlara daha iyi hizmet verebilmesi için bu teknolojilerin nasıl iyileştirilebileceğine dair fikir sunmaktadır. Araştırma, yalnızca otomatik çevirinin yaygın bir kullanımını ortaya koymakla kalmayıp çeviri kalitesini ve kullanıcı deneyimini geliştirmeye yönelik pratik öneriler de sunmuştur. Kuramsal çerçeveleri pratik uygulamalarla birleştiren bu çalışma, çeviri teknolojilerindeki gelecekteki gelişmeleri ve bu teknolojilerin günlük iletişim araçlarına entegrasyonunu tartışmıştır. Bu çalışmanın özgünlüğü, otomatik çevirinin kullanımını ve bu tür teknolojilerin kullanıcı etkileşimleri üzerindeki etkisini ortaya koymasıdır. Araştırma, Airbnb’nin çeviri işlevini destekleyen nöral makine çeviri (NMT veya NMÇ) modellerinin derinlemesine bir analizini içermekte; bu modellerin yetilerini ve sınırlılıklarını incelemektedir. Bu çalışmanın önemi, otomatik çevirilerin küresel iletişimdeki dilsel ve kültürel engelleri nasıl aşabileceğine dair farkındalığımızı geliştirme potansiyeli ve bu mekanizmaların iç işleyişini açıklamasında görülebilir.
Anahtar Kelimeler
Airbnb otomatik çeviri kültürlerarası iletişim nöral makine çevirisi çeviri teknolojileri
Kaynakça
- Airbnb Newsroom (2021). “Translation engine launches reviews after expanding to messages this summer”. Retrieved from https://news.airbnb.com/translation-engine-launches-reviewsafter-expanding-to-messages-this-summer/.
- Academia.edu (n.d.). “Translating cultural nuances: Challenges and strategies”. Retrieved from https://www.academia.edu/107842485/Translating_Cultural_Nuances_Challenges_and_Strategies.
- Bal, Dilara & Demirel Fakiroğlu, Gözde. (2023). Orthographic Errors in English Abstracts Written by Turkish Researchers. Dilbilim(40), 89-96.
- Brown, Tom B. et al. (2020). “Language models are few-shot learners”, Advances in Neural Information Processing Systems, 33, 1877–1901.
- Castilho, Sheila et al. (2018). “Integrating neural machine translation into translation workflows”, Machine Translation, 32(4), 275–298.
- DeepAI. (n.d.). “Neural machine translation”. Retrieved from [https://deepai.org/machine-learningmodel/neural-machine-translation]
- Gutiérrez, Javier et al. (2017). “The eruption of Airbnb in tourist cities: Comparing spatial patterns of hotels and peer-to-peer accommodation in Barcelona”, Tourism Management, 62, 278–291.
- Guttentag, Daniel (2019). “Airbnb: Disruptive innovation and the rise of an informal tourism accommodation sector”, Current Issues in Tourism, 22(9), 1011–1035.
- iTranslate. (n.d.). “An introduction to the architecture of neural machine translation”. Retrieved from https://itranslate.com/blog/an-introduction-to-the-architecture-of-neural-machinetranslation
- Jiménez-Crespo, Miguel Ángel (2017). Crowdsourcing and online collaborative translations: Expanding the limits of translation studies. Amsterdam: John Benjamins Publishing.
- Odacıoğlu, Mehmet Cem and Köktürk, Şaban (2015). “A critical approach to the concept of localization”, Journal of Translation Studies, 19(1), 23-45.
- Onete, Bogdan C. et al. (2018). “Social media-based travel services: Airbnb case”, Management Dynamics in the Knowledge Economy, 6(2), 245-265. OpenNMT (n.d.). “Open-source neural machine translation”.
- Özen, İbrahim A. et al. (2023). “Exploring host-guest interaction on Airbnb”, Revista Rosa dos Ventos - Turismo e Hospitalidade, 15(3), 626–649.
- Şahin, Mehmet (2023). Yapay çeviri. İstanbul: Çeviribilim Yayınları.
- Skift (2021). “Airbnb deploys translation engine to let guests see reviews in 60 languages”. Retrieved from https://skift.com/2022/09/08/airbnb-deploys-translation-engine-to-let-guests-seereviews-in-60-languages
- Telaumbanua, Yasminar Amaerita et al. (2024). “Analysis of two translation applications: Why is DeepL Translate more accurate than Google Translate?”, International Journal of Linguistics and Translation Studies, 10(2), 45–60.
- The AI Limited (2023). “Multilingual NLP: Tackling the challenges of machine translation in lowresource languages”. Retrieved from https://theailimited.com/multilingual-nlp-tackling-thechallenges-of-machine-translation-in-low-resource-languages.
- Toral, Antonio et al. (2018). “Post-editing effort of neural and statistical machine translation: A study on English to Dutch”, Computational Linguistics, 44(3), 445-469.
An Analysis of Neural Machine Translation on Cross-Cultural Communication: A Case Study of Airbnb's Translation Function
Öz
This study examines the theoretical and practical implications of the online platform Airbnb's automatic translation function, which enables cross-cultural communication without users needing to know each other’s language. Users are unaware of the language used during the communication between parties on this application and website, thus making it seamless to communicate without speaking the same language. The research encompasses an in-depth analysis of the neural machine translation (NMT) models powering Airbnb's translation function, exploring their capabilities and limitations. Methodologically, the study has employed a content analysis of the website’s language and translation policies as well as a combination of case studies to evaluate the user experience and linguistic accuracy of the translations. The findings of this study highlight both the strengths and areas for improvement in current translation technologies, offering insights into how they can be refined to serve global platforms better. This research has also revealed a common use of automatic translation and provides practical recommendations for enhancing the quality and user experience. By bridging theoretical frameworks with practical applications, this study aims to discuss future developments in translation technologies and their integration into everyday communication tools. The originality of this work lies in exposing the use of automatic translation and the impact of such technologies on user interactions. The significance of this study is underscored by its potential to enhance our understanding of how automated translations can bridge linguistic and cultural gaps in global communication and explain the inner workings of such automatic translation mechanisms.
Anahtar Kelimeler
Airbnb automatic translation cross-cultural communication neural machine translation translation technologies
Kaynakça
- Airbnb Newsroom (2021). “Translation engine launches reviews after expanding to messages this summer”. Retrieved from https://news.airbnb.com/translation-engine-launches-reviewsafter-expanding-to-messages-this-summer/.
- Academia.edu (n.d.). “Translating cultural nuances: Challenges and strategies”. Retrieved from https://www.academia.edu/107842485/Translating_Cultural_Nuances_Challenges_and_Strategies.
- Bal, Dilara & Demirel Fakiroğlu, Gözde. (2023). Orthographic Errors in English Abstracts Written by Turkish Researchers. Dilbilim(40), 89-96.
- Brown, Tom B. et al. (2020). “Language models are few-shot learners”, Advances in Neural Information Processing Systems, 33, 1877–1901.
- Castilho, Sheila et al. (2018). “Integrating neural machine translation into translation workflows”, Machine Translation, 32(4), 275–298.
- DeepAI. (n.d.). “Neural machine translation”. Retrieved from [https://deepai.org/machine-learningmodel/neural-machine-translation]
- Gutiérrez, Javier et al. (2017). “The eruption of Airbnb in tourist cities: Comparing spatial patterns of hotels and peer-to-peer accommodation in Barcelona”, Tourism Management, 62, 278–291.
- Guttentag, Daniel (2019). “Airbnb: Disruptive innovation and the rise of an informal tourism accommodation sector”, Current Issues in Tourism, 22(9), 1011–1035.
- iTranslate. (n.d.). “An introduction to the architecture of neural machine translation”. Retrieved from https://itranslate.com/blog/an-introduction-to-the-architecture-of-neural-machinetranslation
- Jiménez-Crespo, Miguel Ángel (2017). Crowdsourcing and online collaborative translations: Expanding the limits of translation studies. Amsterdam: John Benjamins Publishing.
- Odacıoğlu, Mehmet Cem and Köktürk, Şaban (2015). “A critical approach to the concept of localization”, Journal of Translation Studies, 19(1), 23-45.
- Onete, Bogdan C. et al. (2018). “Social media-based travel services: Airbnb case”, Management Dynamics in the Knowledge Economy, 6(2), 245-265. OpenNMT (n.d.). “Open-source neural machine translation”.
- Özen, İbrahim A. et al. (2023). “Exploring host-guest interaction on Airbnb”, Revista Rosa dos Ventos - Turismo e Hospitalidade, 15(3), 626–649.
- Şahin, Mehmet (2023). Yapay çeviri. İstanbul: Çeviribilim Yayınları.
- Skift (2021). “Airbnb deploys translation engine to let guests see reviews in 60 languages”. Retrieved from https://skift.com/2022/09/08/airbnb-deploys-translation-engine-to-let-guests-seereviews-in-60-languages
- Telaumbanua, Yasminar Amaerita et al. (2024). “Analysis of two translation applications: Why is DeepL Translate more accurate than Google Translate?”, International Journal of Linguistics and Translation Studies, 10(2), 45–60.
- The AI Limited (2023). “Multilingual NLP: Tackling the challenges of machine translation in lowresource languages”. Retrieved from https://theailimited.com/multilingual-nlp-tackling-thechallenges-of-machine-translation-in-low-resource-languages.
- Toral, Antonio et al. (2018). “Post-editing effort of neural and statistical machine translation: A study on English to Dutch”, Computational Linguistics, 44(3), 445-469.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Çeviri ve Yorum Çalışmaları |
| Bölüm | ÇEVİRİBİLİM / ARAŞTIRMA MAKALELERİ |
| Yazarlar | |
| Erken Görünüm Tarihi | 30 Nisan 2025 |
| Yayımlanma Tarihi | 30 Nisan 2025 |
| Gönderilme Tarihi | 15 Aralık 2024 |
| Kabul Tarihi | 24 Mart 2025 |
| Yayımlandığı Sayı | Yıl 2025 Cilt: 10 Sayı: 1 |
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.